Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction.

ABSTRACT: Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. Here, we introduce MCGLPPI, a geometric representation learning framework that combines graph neural networks (GNNs) with MARTINI molecular coarse-grained (CG) models to predict PPI overall properties accurately and efficiently. Extensive experiments on three types of downstream PPI prop

SUBMITTER: Yue Y 

PROVIDER: S-EPMC11544137 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature